Bazinga! A Dataset for Multi-Party Dialogues Structuring
Paul Lerner, Juliette Bergoënd, Camille Guinaudeau, Hervé Bredin, Benjamin Maurice, Sharleyne Lefevre, Martin Bouteiller, Aman Berhe, Léo Galmant, Ruiqing Yin, Claude Barras · 2022
We introduce a dataset built around a large collection of TV (and movie) series.Those are filled with challenging multi-party dialogues.Moreover, TV series come with a very active fan base that allows the collection of metadata and accelerates annotation.With 16 TV and movie series, Bazinga! amounts to 400+ hours of speech and 8M+ tokens, including 500K+ tokens annotated with the speaker, addressee, and entity linking information.Along with the dataset, we also provide a baseline for speaker diarization, punctuation restoration, and person entity recognition.The results demonstrate the difficulty of the tasks and of transfer learning from models trained on mono-speaker audio or written text, which is more widely available.This work is a step towards better multi-party dialogue structuring and understanding.Bazinga! is available at hf.co/bazinga.Because (a large) part of Bazinga! is only partially annotated, we also expect this dataset to foster research towards self-or weakly-supervised learning methods.